Epigenetics, the study of non-DNA sequence-related heredity, is at the epicenter of modern medicine because it can help to explain the relationship between an individual's genetic background, the environment, aging, and disease. The Center for the Epigenetics of Common Human Disease was created four years ago to begin to develop the interface between epigenetics and epidemiologic-based phenotype studies, recognizing that epigenetics requires new ways of thinking about disease. We used the reduced funding of the current grant cycle to create a highly interdisciplinary group of faculty and trainees, including molecular biologists, biostatisticians, epidemiologists, and clinical investigators. Working together, we have developed novel approaches to genome-wide DNA methylation (DNAm) analysis, allele-specific expression, and new statistical epigenetic tools. Using these tools, we discovered that most variable DNAm is in neither CpG islands nor proelationship between epigenetic variation, genetic variation, environment and phenotype. We will continue to pioneer genome-wide epigenetic technology that is cost effective for large scale analysis of population-based samples, applying our knowledge from the current period to second-generation sequencing for epigenetic measurement, including DNAm and allele-specific methylation. We will continue to pioneer new statistical approaches for quantitative and binary DNAm assessment in populations, including an Epigenetic Barcode. We will develop Foundational Epigenetic Epidemiology, examining: time-dependence, heritability and environmental relationship of epigenetic marks;heritability in MZ and DZ twins;and develop an epigenetic transmission disequilibrium test. We will then pioneer Etiologic Epigenetic Epidemiology, by integrating novel genome-wide methylation scans (GWMs) with existing Genome-Wide Association Study (GWAS) and epidemiologic phenotype data, a design we term Genome-Wide of disease.

Public Health Relevance

- Most common human disease is caused by a combination of genetic, environmental, and epigenetic factors, the latter of which is non-sequence information that controls gene function and mediates the relationship between genes and the environment. We are creating the new field of epigenetic epidemiology by providing the experimental tools, statistical methods, and approaches to key barriers that have prevented incorporation of epigenetics into research on diseases such as bipolar disorder, autism and age-related illness.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZHG1-HGR-N (J1))
Program Officer
Brooks, Lisa
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Johns Hopkins University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Wilton, Richard; Li, Xin; Feinberg, Andrew P et al. (2018) Arioc: GPU-accelerated alignment of short bisulfite-treated reads. Bioinformatics 34:2673-2675
Vandiver, Amy R; Idrizi, Adrian; Rizzardi, Lindsay et al. (2015) DNA methylation is stable during replication and cell cycle arrest. Sci Rep 5:17911
Ladd-Acosta, Christine (2015) Epigenetic Signatures as Biomarkers of Exposure. Curr Environ Health Rep 2:117-25
Feinberg, Andrew P; Fallin, M Daniele (2015) Epigenetics at the Crossroads of Genes and the Environment. JAMA 314:1129-30
Aryee, Martin J; Jaffe, Andrew E; Corrada-Bravo, Hector et al. (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363-9
Ladd-Acosta, C; Hansen, K D; Briem, E et al. (2014) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry 19:862-71
Plongthongkum, Nongluk; van Eijk, Kristel R; de Jong, Simone et al. (2014) Characterization of genome-methylome interactions in 22 nuclear pedigrees. PLoS One 9:e99313
Feinberg, Andrew P (2014) The nucleolus gets the silent treatment. Cell Stem Cell 15:675-6
Liu, Yun; Li, Xin; Aryee, Martin J et al. (2014) GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease. Am J Hum Genet 94:485-95
Jaffe, Andrew E; Storey, John D; Ji, Hongkai et al. (2013) Gene set bagging for estimating the probability a statistically significant result will replicate. BMC Bioinformatics 14:360

Showing the most recent 10 out of 40 publications